Three PhD Positions in Computational Astrophysics 80 %

University of Zurich; Zürich; On-site; CHF 90,000 - 110,000; Full time

Job summary

The University of Zurich invites applications for a research position in astrophysics and computational methods. The role focuses on flagship simulations, emulators, and multi-probe cosmology, using GPU-accelerated supercomputers and ML models to generate mock catalogues for Euclid.

Candidates should hold a master's in physics or astronomy; strong interest in HPC, machine learning or numerical astrophysics; programming experience is an asset.

Qualifications

• Master's degree in physics, astronomy or related field is required by start date.
• Candidates with strong interest in HPC, ML or numerical astrophysics encouraged to apply.

Responsibilities

• Develop and run flagship simulations using GPU-accelerated supercomputers.
• Build emulators: ML models predicting cosmological statistics.
• Model baryonification and multi-probe cosmology pipelines for synthetic sky maps.

Skills

High-performance computing

Machine learning

Numerical astrophysics

Programming experience

Education

Master's degree in physics/astronomy or related field

Job description

The University of Zurich, Switzerland's largest university, offers a range of attractive positions in various subject areas and professional fields. With around 10,000 employees and currently 12 professional apprenticeship streams the University offers an inspiring working environment on cutting-edge research and top-class education. Put your talent and skills to work with us. Find out more about UZH as an employer!

Your responsibilities

The research covers three broad, overlapping areas:

• Flagship simulations: Develop and run the next-generation Euclid Flagship simulation with PKDGRAV3 on GPU-accelerated supercomputers. This includes extending the code to identify dark matter halos and their substructure on the fly, and working with our partners to turn the simulation into mock galaxy catalogues for Euclid.
• Emulators: Build the next generation of the Euclid Emulator: machine-learning models, trained on large suites of simulations, that predict cosmological statistics in milliseconds. The goal is to extend the emulator from the matter distribution to galaxy clustering and lensing statistics.
• Baryonification and multi-probe cosmology: Model how gas and stars alter the matter distribution predicted by N-body simulations, and develop a pipeline that produces consistent synthetic sky maps (weak lensing, galaxy clustering, X-ray and Sunyaev–Zel'dovich) for Euclid and complementary surveys.

This is a unique opportunity to work at the intersection of astrophysics and advanced computational methods.

Your profile

Applicants are expected to hold a master's degree (or equivalent) in physics, astronomy or a related field by the starting date.

Candidates with a strong interest in high-performance computing, machine learning or numerical astrophysics are especially encouraged to apply. Programming experience is an asset.